1. TRANSITION FROM LUMPED TO DISTRIBUTED SYSTEMS Victor Koren, Michael Smith, Seann Reed, Ziya Zhang NOAA/NWS/OHD/HL, Silver Spring, MD

2. Distributed and Lumped Modeling Dynamics

3. History Lessons There is a similarity in dynamics of lumped and distributed model developments There is a large delay between model development and application There is no ‘unique best’ model. Selection for application is rather arbitrary process that depends on an expertise of the user and practical requirements Most successful models in an operational use are models which have well developed parameterization tools

4. Distinguishing Features of Lumped and Distributed Models Physics Does point rainfall-runoff model represent well ‘field’ processes Can hillslope/channel routing be represented well on ‘practically reasonable’ space/time scales Does statistical approach solve a basin heterogeneity problem

5. Distinguishing Features of Lumped and Distributed Models (Continued) Physics Does statistical approach solve a basin heterogeneity problem Surface runoff simulated with and without use of rainfall distribution function at different scales

6. Distinguishing Features of Lumped and Distributed Models (Continued) Space/Time Variability Does accounting for the space/time variability of input data and parameters guarantee better results Does scale effect significantly on the model structure Is a lumped model a reasonable candidate in a distributed system Effect of noisy rainfall data on the peak volume at different simulation scales.

7. Distinguishing Features of Lumped and Distributed Models (Continued) Parameterization/Calibration Can distributed model parameters be measured on the grid scale Are distributed model parameters identifiable enough from hydrograph analyses How much does scale effect on model parameters Change an ‘effective’ parameter value at different scales as a function of rainfall variability

8. HL-Research Modeling System (HL-RMS) Modeling framework for testing lumped, semi-distributed, and fully distributed hydrologic modeling approaches

9. HL-RMS Structure Uses channel connectivity matrix defined on the HRAP grid Each computational element consists of a number of uniform hillslopes and ‘conceptual’ channels Rainfall-runoff component (Sacramento model in the 1st version) generates ‘fast’ and ‘slow’ runoffs Hillslope transforms (kinematic routing) ‘fast’ runoff into lateral channel inflow Channel inflow combined with ‘slow’ runoff and upstream cell outflow is routed through a cell ‘conceptual’ channel Ingests NEXRAD Stage III data Includes features of lumping parameters/input data Modular design to test other models

11. HL-RMS Structure Conceptualization of a grid cell

12. HL-RMS Parameterization A priori parameters: Rainfall-runoff model parameter grids are estimated using soil/vegetation data Hillslope/Channel routing parameter grids, slope, length, area above, are calculated based on DEM Uniform channel shape and roughness coefficient is assumed at each grid cell Parameter adjustment: Scaling/Replacement based on lumped or semi- distributed calibration of rainfall-runoff model Spatially variable channel shape and roughness parameters can be generated from discharge measurements at outlets and geomorphological properties at each grid cell